In today's society, end users regularly utilize smartphones, speech recognition systems, and other audio-based technologies to place and receive phone calls, access various types of internet services, and perform a variety of functions, or a combination thereof. When an end user communicates using such devices and technologies, the end user may be located in an environment that may not provide for optimal communication conditions. For example, the environment may include ambient noises, such as, but not limited to, noises generated from land vehicles, noises generated by aircraft, noises generated by machinery, and noises generated by animals. Additionally, the environment may also include other competing users, who may be speaking or otherwise making sounds that may interfere with the communications made by the end user.
Currently, in order to counteract such ambient noise and noise generated from competing users, noise suppression algorithms and technologies have been utilized to separate a targeted end user's audio signals from the ambient noise and from the competing users' interfering audio signals. However, current noise suppression algorithms and technologies often do so only by predefining the locations of the sources of the ambient noise, the location of the targeted end user, and the locations of competing users. Additionally, current technologies require the use of a defined time period to phase in and adapt to various provided constraints. Furthermore, current technologies fail to use any knowledge of the end user or the acoustic environment associated with the end user. As a result, current noise adaptation processes often require significant amounts of time, are often suboptimal based on computational and algorithmic constraints, and often require significant usage of limited resources.